import gradio as gr
import requests
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
import os
import torch
from peft import PeftModel
import transformers
## CoT prompts
def _add_markup(table):
parts = [p.strip() for p in table.splitlines(keepends=False)]
if parts[0].startswith('TITLE'):
result = f"Title: {parts[0].split(' | ')[1].strip()}\n"
rows = parts[1:]
else:
result = ''
rows = parts
prefixes = ['Header: '] + [f'Row {i+1}: ' for i in range(len(rows) - 1)]
return result + '\n'.join(prefix + row for prefix, row in zip(prefixes, rows))
_TABLE = """Year | Democrats | Republicans | Independents
2004 | 68.1% | 45.0% | 53.0%
2006 | 58.0% | 42.0% | 53.0%
2007 | 59.0% | 38.0% | 45.0%
2009 | 72.0% | 49.0% | 60.0%
2011 | 71.0% | 51.2% | 58.0%
2012 | 70.0% | 48.0% | 53.0%
2013 | 72.0% | 41.0% | 60.0%"""
_INSTRUCTION = 'Read the table below to answer the following questions.'
_TEMPLATE = f"""First read an example then the complete question for the second table.
------------
{_INSTRUCTION}
{_add_markup(_TABLE)}
Q: In which year republicans have the lowest favor rate?
A: Let's find the column of republicans. Then let's extract the favor rates, they [45.0, 42.0, 38.0, 49.0, 51.2, 48.0, 41.0]. The smallest number is 38.0, that's Row 3. Row 3 is year 2007. The answer is 2007.
Q: What is the sum of Democrats' favor rates of 2004, 2012, and 2013?
A: Let's find the rows of years 2004, 2012, and 2013. We find Row 1, 6, 7. The favor dates of Demoncrats on that 3 rows are 68.1, 70.0, and 72.0. 68.1+70.0+72=210.1. The answer is 210.1.
Q: By how many points do Independents surpass Republicans in the year of 2011?
A: Let's find the row with year = 2011. We find Row 5. We extract Independents and Republicans' numbers. They are 58.0 and 51.2. 58.0-51.2=6.8. The answer is 6.8.
Q: Which group has the overall worst performance?
A: Let's sample a couple of years. In Row 1, year 2004, we find Republicans having the lowest favor rate 45.0 (since 45.0<68.1, 45.0<53.0). In year 2006, Row 2, we find Republicans having the lowest favor rate 42.0 (42.0<58.0, 42.0<53.0). The trend continues to other years. The answer is Republicans.
Q: Which party has the second highest favor rates in 2007?
A: Let's find the row of year 2007, that's Row 3. Let's extract the numbers on Row 3: [59.0, 38.0, 45.0]. 45.0 is the second highest. 45.0 is the number of Independents. The answer is Independents.
{_INSTRUCTION}"""
## alpaca-lora
assert (
"LlamaTokenizer" in transformers._import_structure["models.llama"]
), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
BASE_MODEL = "decapoda-research/llama-7b-hf"
LORA_WEIGHTS = "tloen/alpaca-lora-7b"
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
try:
if torch.backends.mps.is_available():
device = "mps"
except:
pass
if device == "cuda":
model = LlamaForCausalLM.from_pretrained(
BASE_MODEL,
load_in_8bit=False,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True
)
elif device == "mps":
model = LlamaForCausalLM.from_pretrained(
BASE_MODEL,
device_map={"": device},
torch_dtype=torch.float16,
)
model = PeftModel.from_pretrained(
model,
LORA_WEIGHTS,
device_map={"": device},
torch_dtype=torch.float16,
)
else:
model = LlamaForCausalLM.from_pretrained(
BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True
)
model = PeftModel.from_pretrained(
model,
LORA_WEIGHTS,
device_map={"": device},
)
if device != "cpu":
model.half()
model.eval()
if torch.__version__ >= "2":
model = torch.compile(model)
def evaluate(
table,
question,
input=None,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
max_new_tokens=128,
**kwargs,
):
prompt = _TEMPLATE + "\n" + _add_markup(table) + "\n" + "Q: " + question + "\n" + "A:"
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
#return output.split("A:")[-1].strip()
return output
model_deplot = Pix2StructForConditionalGeneration.from_pretrained("google/deplot")
processor_deplot = Pix2StructProcessor.from_pretrained("google/deplot")
def process_document(image, question):
# image = Image.open(image)
inputs = processor_deplot(images=image, text="Generate the underlying data table for the figure below:", return_tensors="pt")
predictions = model_deplot.generate(**inputs, max_new_tokens=512)
table = processor_deplot.decode(predictions[0], skip_special_tokens=True)
# send prompt+table to LLM
res = evaluate(table.replace("<0x0A>", "\n"), question)
return res + "\n\n" + res.split("A:")[-1]
description = "Demo for deplot+llm for QA or summarisation. To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2212.10505' target='_blank'>DePlot: One-shot visual language reasoning by plot-to-table translation</a></p>"
demo = gr.Interface(
fn=process_document,
inputs=["image", "text"],
outputs="text",
title="Demo: deplot+llm test",
description=description,
article=article,
enable_queue=True,
examples=[["example_1.png", "When is the coffee break?"], ["example_2.jpeg", "What's the population of Stoddard?"]],
cache_examples=False)
demo.launch()